AI Factory Use Cases v1.3.1
AI Factory is designed to help you build Sovereign AI solutions — where your data, models, and AI-driven applications operate under your governance, in your infrastructure, with your trusted content.
These use cases highlight how AI Factory components work together to support real-world, production-ready Sovereign AI applications — across industries and solution patterns.
For industry-specific solution examples, see: Industry Solutions.
Retrieval-Augmented Generation (RAG)
RAG is a foundational pattern for Sovereign AI — it lets you deliver accurate, grounded responses by combining LLMs with your enterprise data.
What
- Use AI models that retrieve trusted content from your Knowledge Bases, rather than relying only on opaque base models.
- Make responses auditable and explainable — key for Sovereign AI.
Why
- Reduce hallucinations.
- Maintain compliance and traceability.
- Ensure AI answers reflect your latest business knowledge.
How
- Build Knowledge Bases from your Data Sources.
- Implement tuned Retrievers for semantic search.
- Power Assistants and apps with RAG pipelines.
Learn More
Document Intelligence Pipelines
Automate the transformation of documents into structured, queryable knowledge that remains inside your governance boundary.
What
- Apply OCR, parsing, summarization, and metadata extraction.
- Store results in Knowledge Bases or Vector Engine.
Why
- Keep sensitive documents in your infrastructure — no third-party API calls required.
- Build explainable pipelines for compliance-heavy domains.
How
- Use Pipelines.
- Leverage built-in Preparers (OCR, parse PDF, parse HTML).
- Index results for search or RAG.
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Conversational Assistants and Chatbots
Build Assistants that run within your infrastructure, use your models, and rely on your Knowledge Bases — with complete visibility and control.
What
- AI Assistants powered by Model Serving and your RAG pipeline.
- Defined and governed with Rulesets.
Why
- Avoid relying on public API services for LLM responses.
- Control tone, style, compliance — essential for Sovereign AI.
How
- Build Assistants in Agent Studio.
- Govern behavior with Rulesets.
- Add Tools to safely integrate internal systems.
Learn More
- Agent Studio in Hybrid Manager
- Sovereign AI Explained
- AI Factory 101
Semantic Search Across Enterprise Content
Enable natural language search on your content — while ensuring embeddings and search data remain inside your control.
What
- Vector-based search on Knowledge Bases or Vector Engine.
- Supports text, hybrid structured/unstructured data, images.
Why
- Keep embeddings in your databases or object storage — not in third-party vector DBs.
- Maintain auditability and security.
How
- Build Knowledge Bases.
- Use Retrievers to expose search to Assistants or apps.
- Use Vector Engine for in-database search.
Learn More
Real-Time Model Inference APIs
Deploy and serve models inside your Kubernetes clusters, under your governance — not through opaque third-party APIs.
What
- Serve LLMs, embeddings, vision models via KServe-based Model Serving.
- Models pulled from your Model Library, deployed via InferenceService.
Why
- Full control of model stack and model versions.
- Run inference in GPU-accelerated, air-gapped environments if needed.
How
- Manage models in Model Library.
- Deploy with Model Serving.
- Secure endpoints with Hybrid Manager controls.
Learn More
Automated AI-Powered ETL Pipelines
Keep AI data pipelines inside your controlled environment — with no data leakage.
What
- Use Structures and Pipelines to run:
- AI categorization
- Document summarization
- Metadata extraction
- Data cleansing
Why
- Avoid sending sensitive data to cloud APIs.
- Build reusable, explainable ETL components.
How
- Build Structures.
- Run as Tools in Assistants or standalone Pipelines.
- Monitor and govern pipeline execution via Hybrid Manager.
Learn More
Summary
Sovereign AI use cases are about keeping data, models, and AI applications under your governance:
- Build RAG pipelines grounded in your enterprise data
- Automate document intelligence workflows
- Deploy conversational Assistants in your infrastructure
- Serve AI models via GPU-backed Model Serving
- Run AI-powered ETL pipelines with no data leaving your environment
- Power enterprise semantic search with full control of embeddings
Next steps:
- Explore Industry Solutions
- Follow a Learning Path
- Get started with How-To Guides
AI Factory gives you the foundation to build trusted, governed, explainable Sovereign AI — on your terms.
→ Start today with AI Factory 101, or deploy your first Assistant.